The layered double hydroxides (LDH) of Mg 2 AlNi and Mg 3 Al pillared by Keggin-type phosphotungstic acid anion (POM), i.e. Mg 2 AlNi-POM LDH and Mg 3 Al-POM LDH were synthesized by an ion-exchange method. The synthesized POM intercalated LDH compounds were characterized using various techniques such as FTIR, XRD, TGA and BET. The observed results show that the obtained catalysts retain the layer structure of LDH. Compared with the binary Mg 3 Al-POM LDH, the ternary Mg 2 AlNi-POM LDH catalyst indicated a higher thermal and chemical stability. The catalytic activity of the resulting LDH-POM was also assessed in the green oxidation of cyclohexanol with aqueous H 2 O 2 as an oxidant. The Mg 2 AlNi-POM LDH showed a much higher conversion and selectivity for cyclohexanone than the corresponding Mg 3 Al-POM LDH catalyst.
Background: The forecasting of daily outpatient visits has significant practical implications in outpatient clinic operation management, not only contributing to guiding long-term resource planning and scheduling but also making tactical resolutions for short-term adjustments on special days such as holidays. We here in propose an effective genetic programming (GP)-based forecasting model to predict daily outpatient visits (OV) in a primary hospital.
Methods: In the GP-based model, the holiday-based distance outlier mining algorithm was used to determine the holiday effect. In addition, solar terms were applied as the smallest unit to more accurately determine the impact of a change in the climate on the outpatient volume. A segmental learning strategy also was used to predict the daily outpatient volume for the time series data.
Results: The GP-based prediction could more effectively extract depth information from a finite training sample size and achieve a better performance for predicting daily outpatient visits, with lower root mean square error (RMSE) and higher coefficient of determination (R2) values, than the seasonal autoregressive integrated moving average (SARIMA) model in the time range of holidays and the holiday effect.
Conclusion: GP-based model can achieve better prediction performance by overcoming the shortcomings of the SARIMA model. The results can be applied to support decision-making and planning of outpatient clinic resources, to help managers implement periodic scheduling of available resources on the basis of periodic features, and to perform proactive scheduling of additional resources.
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